Static Sensor Research Articles

A Mobile Sensors Dispatch Scheme Based on Improved SOM Algorithm for Coverage Hole Healing

The coverage holes caused by sensor failures severely degraded quality of service (QoS) in the sensing layer of Internet of Things (IoT). To improve the reliability and QoS of sensing layer, coverage hole healing problem has drawn considerable attention among researchers. Coverage holes healing problem aims to select and dispatch a set of mobile sensors to coverage holes detected by static sensors. In this paper, an improved Self-Organized Mapping (SOM) is proposed for coverage holes healing in hybrid sensor network composed of static sensors and mobile sensors. Firstly, the fuzzy system is introduced to optimize the selection strategy in SOM. The improved strategy assigns eligibility to each mobile sensor, which is calculated by the moving distance of mobile sensors and the residual energy of mobile sensors. The mobile sensor with the highest eligibility is selected. Secondly, a priority decision model is designed to measure the importance of failed sensors in sensor network. This model analyzes the location information and energy factor of failed sensors. Finally, according to failed sensors with different priorities, a new learning parameter is designed. The proposed scheme can adaptively update the weights of the winner in SOM and it can efficiently dispatch mobile sensors while maintaining the reliability of the network. Simulation results demonstrate that the proposed scheme can obtain better performance than the existing algorithm in aspects of efficient coverage hole healing, energy consumption, and network lifetime.

LIDAR Scanning as an Advanced Technology in Physical Hydraulic Modelling: The Stilling Basin Example

In hydraulic engineering, stilling basin design is traditionally carried out using physical models, conducting visual flow observations as well as point-source measurements of pressure, flow depth, and velocity at locations of design relevance. Point measurements often fail to capture the strongly varying three-dimensionality of the flows within the stilling basin that are important for the best possible design of the structure. This study introduced fixed scanning 2D LIDAR technology for laboratory-scale physical hydraulic modelling of stilling basins. The free-surface motions were successfully captured along both longitudinal and transverse directions, providing a detailed free-surface map. LIDAR-derived free-surface elevations were compared with typical point-source measurements using air–water conductivity probes, showing that the elevations measured with LIDAR consistently corresponded to locations of strongest air–water flow interactions at local void fractions of approximately 50%. The comparison of LIDAR-derived free-surface elevations with static and dynamic pressure sensors confirmed differences between the two measurement devices in the most energetic parts of the jump roller. The present study demonstrates that LIDAR technology can play an important role in physical hydraulic modelling, enabling design improvement through detailed free-surface characterization of complex air–water flow motions beyond the current practice of point measurements and visual flow observations.

Compensation for the Lack of Measured Data on Decisive Cultivation Conditions in Diversified Territories without Losing Correct Information

Sustainable precision agriculture requires site-specific management procedures. This needs appropriate information combining traditional measured data and mapped conditions, models, and specific interpretation. It is impossible to cover the entire variety of sites in the territory with measuring devices, and therefore the measured data are insufficient for a detailed description of changing conditions on each geographical unit. However, detailed data on the morphology and pedologic conditions are usually available, and their synthesis creates the basis for detailed interpolation of the entire area’s measured data and mapping. This article presents a procedure for the synthesis of morphometric and soil indices resulting in the definition and mapping of morpho-pedotops, the interpretation of their thermal–moisture condition, and, consequently, the comparison of these conditions with the condition on the sites with installed sensor stations. This procedure enables reasonable logic interpolation of the measured microclimatic data by sensor stations to the whole study area. The result is the definition of the thermal–moisture condition of the whole territory in comparison to the measured sites. Therefore, the results provide the basis for interpolation for the forecast of climatic events developed for the sites of sensor stations to the whole study area and the forecast of temporal disease events, and thus the basis for precise site-specific field management interventions, even in the case of the lack of the whole area covering measured data.

Evaluation of Spatio-Temporal Soil Moisture Variability in Semi-Arid Rangeland Ecosystem, Maasai Mara National Reserve, Kenya

Aim: To evaluate the spatio-temporal soil moisture storage and retention capacities in semi-arid rangeland ecosystem, Maasai Mara National Reserve (MMNR), Kenya
 Study Design: Randomized complete block design (RCBD) of reference Cosmic Ray Neutron Sensor (CRNS) station, ten-(10) spatially distributed (soil moisture and temperature capacitance) probes (5TM-ECH20) sites.
 Place and Duration of Study: Kenya, MMNR, the oldest natural semi-arid rangeland ecosystem and globally unique for the great wildebeest migration, between May 2017 and April 2019.
 Methodology: Soil moisture (SM) variation data was collected using (CRNS) at spatial and point-scale 5TM-ECH2O probes, and gravimetric water content from (10) spatially distributed stations. Both CRNS and 5TM-ECH2O probes were used to monitor near-real time moisture levels at different soil layers ranging between 0-5cm, 5-10cm, 15-20cm, 35-40cm, and 75-80cm. Soil physical and chemical properties were laboratory analyzed. Calibration and validation datasets were obtained from 5TM-ECH2O probe and gravimetric soil samples extracted from respective layers and sites.
 Results: The pedological characteristics of the investigated ecosystem soil profile indicate decreased bulk density by 2.1% to 11.12% from upper layers (0-5cm) to deeper layers at (75–80 cm). Across the rangeland, 70% of soil textural classes were sandy clay loam (SCL) with higher clay percent and 30% sandy clay (SC) and soil porosity varied between 30.1% and 51% in the ecosystem. Moreover, volumetric
 
 
 
 
 
 
 
 
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 water content (VWC) of spatially distributed 5TM-ECH2O probes ranged between 0.11m3m-3 and 0.32m3m-3 during wet season with mean VWC of 0.16m3m-3, however, the VWC ranged between 0.04 m3m-3 and 0.17m3m-3 during the dry season with a mean volume of 0.11m3m-3 across the rangeland ecosystem.
 Conclusion: In this study, SM exhibited an annual periodicity of seasonal variation of spatial and temporal moisture partitioned as moisture gaining, losing, and a moisture stable period. This probably could be a consequence of increased movement of water to deeper layers caused by high precipitation and less evaporative demand caused by lower temperatures. The calibrated CRNS probe provided good estimates of spatial soil moisture variation when calibrated with 5TM-ECH20 and gravimetric sampling in relation to precipitation events and that deeper soil layers showed higher amount of soil moisture than shallow layers. The findings of the study will provide better formulation of the ecosystem vegetation management policies, conservation and planning for sustainable wildlife tourism industry.

ReapIoT: Reliable, Energy-Aware Network Protocol for Large-Scale Internet-of-Things (IoT) Applications

A new era is emerging where buildings, vehicles, factories, and appliances, among many other entities will communicate with each other over the Internet, with limited or no human interaction. Collectively known as the Internet of Things (IoT), these systems share a common technology foundation. They are built on wireless communication between stationary or mobile large-scale electronic sensor and actuator networks. For the vast majority of IoT applications, energy-efficient operations are a critical requirement since they will use battery-powered devices. Another requirement is communication reliability, due to unattended network operations. We introduce ReapIoT, reliable energy-aware protocol for IoT WSNs. ReapIoT is an energy-efficient network communication protocol that considers reliability as a design parameter and as a constraint. The protocol introduces message classification based on reliability. This is used to implement new energy saving techniques. The protocol also introduces the individualized link power settings algorithm, and the link rating parameter. They are used to optimize the network clusters formation and for intracluster communication, under reliability constraint. Consequently, the protocol optimizes energy consumption while providing a reliable data delivery network.

An optical coherence photoacoustic microscopy system using a fiber optic sensor

In this work, a novel fiber optic sensor based on Fabry–Pérot interferometry is adopted in an optical coherence photoacoustic microscopy (OC-PAM) system to enable high-resolution in vivo imaging. The complete OC-PAM system is characterized using the fiber optic sensor for photoacoustic measurement. After characterization, the performance of the system is evaluated by imaging zebrafish larvae in vivo. With a lateral resolution of 3.4 μm and an axial resolution of 3.7 μm in air, the optical coherence microscopy subsystem visualizes the anatomy of the zebrafish larvae. The photoacoustic microscopy subsystem reveals the vasculature of the zebrafish larvae with a lateral resolution of 1.9 μm and an axial resolution of 37.3 μm. As the two modalities share the same sample arm, we obtain inherently co-registered morphological and vascular images. This OC-PAM system provides comprehensive information on the anatomy and vasculature of the zebrafish larvae. Featuring compactness, broad detection bandwidth, and wide detection angle, the fiber optic sensor enables a large field of view with a static sensor position. We verified the feasibility of the fiber optic sensor for dual-modality in vivo imaging. The OC-PAM system, as a non-invasive imaging method, demonstrates its superiority in the investigation of zebrafish larvae, an animal model with increasing significance in developmental biology and disease research. This technique can also be applied for functional as well as longitudinal studies in the future.

A Coverage Optimization Method for WSNs Based on the Improved Weed Algorithm.

Wireless sensor networks (WSNs) is a multi-hop wireless network composed of a group of static or mobile sensor nodes in the form of self-organization. Uneven distribution of nodes often leads to the problem of over coverage and incomplete coverage of monitoring areas. To solve this problem, this paper establishes a network coverage optimization model and proposes a coverage optimization method based on an improved hybrid strategy weed algorithm (LRDE_IWO). The improvement of the weed algorithm includes three steps. Firstly, the standard deviation of normal distribution based on the tangent function is used as the seed’s new step size in the seed diffusion stage to balance the ability of the global search and local search of weed algorithm. Secondly, to avoid the problem of premature convergence, a disturbance mechanism combining enhanced Levy flight and the adaptive random walk strategy is proposed in the process of seed breeding. Finally, in competition of invasive weed stage, the differential evolution strategy is introduced to optimize the competition operation process and speed up convergence. The improved weed algorithm is applied to coverage optimization of WSNs. The simulation results show that the coverage rate of LRDE_IWO is increased by about 1% to 6% compared with the original invade weed algorithm (IWO) and the differential evolution invasive weed optimization algorithm (DE_IWO), and the coverage rate of the LRDE_IWO algorithm is increased by 4.10%, 2.73% and 1.19%, respectively, compared with the antlion optimization algorithm (ALO), the fruit fly optimization algorithm (FOA) and the gauss mutation weed algorithm (IIWO). The results prove the superiority and validity of the improved weed algorithm for coverage optimization of wireless sensor networks.

Examination of Inspection Method for Outer-side Defect on Ferromagnetic Steel Tube by Velocity Effect Using Insertion-type Static Magnetic Field Sensor

Examination of Inspection Method for Outer-side Defect on Ferromagnetic Steel Tube by Velocity Effect Using Insertion-type Static Magnetic Field Sensor

CO2 and CH4 emission meter in hydroelectric plants on aquatic surface

The recent intensification of human activities has led to a significant increase in the emission of certain gases, called greenhouse gases (GHG), such as carbon dioxide (CO2) and methane (CH4). The Earth’s climate changes together through United Nations Organizations (ONU) to create strategies to reduce the impacts of gases in the Earth’s atmosphere. Of all the GHG generation activities, the energy sector is the one that most contributes to global warming, the reservoirs of hydroelectric plants are potential emitters of greenhouse gases. Considering the importance of developing new policies to slow global warming, the proposer work will help to compare the studies carried out on automated gas emission measurement tool, which allows the identification of an estimated amount of pollution in reservoirs that a hydroelectric plant is capable of to produce and emit to the atmosphere. Currently, this measurement is performed using stationary sensors manually positioned whit the aid of boats. The robotic model allows data collection through gas sensors and moves around the water surface by means of engines and guided by GPS. The prototype was applying to a reservoir at the Mourão Hydroelectric Plant in the city of Campo Mourão, Paraná, Brazil, for accurate and reliable data, it was necessary to consider parameter that influence gas measurements, such as humidity, temperature, and climate. The data collected may contribute to future studies for the development of methods for recovering water conditions, through preservation actions.

Adaptive virtual anchor node based underwater localization using improved shortest path algorithm and particle swarm optimization (PSO) technique

AbstractDetection and accurate position estimation have become an essential task for any static underwater acoustic sensor networks. Many localization schemes have been introduced in the field of terrestrial WSN over the last few decades. These schemes are still not perfectly realizable in a harsh underwater environment due to their erroneous communication channel and infrastructure‐less environment. Limited localization coverage, propagation error, and accuracy are the major drawbacks in acoustic communication. This article proposed an adaptive virtual anchor node based on the improved shortest path algorithm with PSO technique. The main advantages of this proposed scheme are divided into two parts. First, introducing an enhanced shortest path algorithm can reduce the propagation error generated through multi‐hop zigzag movement and improve the coverage. Second, the particle swarm optimization algorithm with virtual anchor nodes can significantly enhance the accuracy by reducing the errors to localize the unknown nodes. Investigational results manifest that the proposed methodology performed better than the Improved DV‐Hop +PSO, IRL‐WOA, and basic DV‐Hop methods as far as their restriction. The proposed method improved 23%, 30%, and 35% better localization error, 10%, 15%, and 22% better relative localization error, and 10%, 18%, and 25% better accuracy respectively.

Analyzing Travel Time Reliability from Sparse Probe Vehicle Data: A Case Study on the Effects of Spatial and Temporal Aggregation

Travel time reliability (TTR) indices have gained considerable attention for evaluating the quality of traffic infrastructure. Whereas TTR measures have been widely explored using data from stationary sensors with high penetration rates, there is a lack of research on calculating TTR from mobile sensors such as probe vehicle data (PVD) which is characterized by low penetration rates. PVD is a relevant data source for analyzing non-highway routes, as they are often not sufficiently covered by stationary sensors. The paper presents a methodology for analyzing TTR on (sub-)urban and rural routes with sparse PVD as the only data source that could be used by road authorities or traffic planners. Especially in the case of sparse data, spatial and temporal aggregations could have great impact, which are investigated on two levels: first, the width of time of day (TOD) intervals and second, the length of road segments. The spatial and temporal aggregation effects on travel time index (TTI) as prominent TTR measure are analyzed within an exemplary case study including three different routes. TTI patterns are calculated from data of one year grouped by different days-of-week (DOW) groups and the TOD. The case study shows that using well-chosen temporal and spatial aggregations, even with sparse PVD, an in-depth analysis of traffic patterns is possible.

Proposal for a Nanomechanical Qubit

Mechanical oscillators have been demonstrated with very high quality factors over a wide range of frequencies. These also couple to a wide variety of fields and forces, making them ideal as sensors. The realization of a mechanically-based quantum bit could therefore provide an important new platform for quantum computation and sensing. Here we show that by coupling one of the flexural modes of a suspended carbon nanotube to the charge states of a double quantum dot defined in the nanotube, it is possible to induce sufficient anharmonicity in the mechanical oscillator so that the coupled system can be used as a mechanical quantum bit. This can however only be achieved when the device enters the ultrastrong coupling regime. We discuss the conditions for the anharmonicity to appear, and we show that the Hamiltonian can be mapped onto an anharmonic oscillator, allowing us to work out the energy level structure and how decoherence from the quantum dot and the mechanical oscillator are inherited by the qubit. Remarkably, the dephasing due to the quantum dot is expected to be reduced by several orders of magnitude in the coupled system. We outline qubit control, readout protocols, the realization of a CNOT gate by coupling two qubits to microwave cavity, and finally how the qubit can be used as a static force quantum sensor.

Delay-Tolerant Sensor Network (DTN) Implementation in Cloud Computing

In this study’s scheme, the objective is to employ the robot to implement the location estimation process for sensor nodes with which it interacts relative to the radio messages’ signal strengths, having received the signals from the sensor nodes. The implication is that the central purpose of the study lies in the elimination of the static sensor nodes’ processing constraints. Imperative to highlight is that the study’s mathematical contribution lies in the utilization of the REKF-based state estimator to analyze the localization problem. Deviating from Kalman Filter, it is worth noting that REKF exhibits computational efficiency and robustness. Indeed, the localization scheme is implemented on a hybrid network test bed.

Under water motion tracking and monitoring using wireless sensor network and Machine learning

Wireless Sensor Networks (WSNs) plays an indispensable role in different application and development in new generation technology. In 5G technology, one of the essential features is the connectivity with registered and unregistered networks to promote remote automation. By which the technology can be harnessed at its best and scaled in multi-dimensional applications. These applications are also enabled for diverse motion detection and object tracking applications. In this presented work, an underwater object tracking technique has been proposed, which enable the prediction of the mobile sensor node. The method involves a simulation which demonstrates a 2D-UASN architecture of underwater scenario and in this network using a different number of nodes using random positioning static sensor nodes are deployed. Finally, a mobile sensor node with random mobility is introduced into the network. The approach follows statistical analysis to compute the next possible position of the node. Based on experiments, the accuracy of the predictive methodology is measured in two different scenarios. In the first scenario, the number of nodes is variable, and the simulation area kept fixed size, i.e., 1000X1000. Additionally, in the second scenario, the number of nodes remains fixed, and the simulation area is variable. The results demonstrate that when we deploy sensor nodes randomly in a large area and network node density is less, the detection accuracy significantly affects and demonstrates low accuracy compared to dense area networks. Secondly, the prediction trend from dense to less dense area demonstrates the reducing accuracy.

Imputing Parking Usage on Sparsely Monitored Areas Within Amsterdam Through the Application of Machine Learning

Effective parking policy is essential for cities to reduce the demand their road networks experience and to combat their carbon footprints. Existing research in the application of machine learning to understand parking behavior assumes that cities have prohibitively expensive stationary parking sensors installed, while no research has yet attempted to use machine learning to impute for parking behavior using mobile probe data of sparsely monitored areas. To this end, this paper shows that it is indeed feasible to impute parking pressure (occupation as a percentage). Gradient boosted trees were found to perform the best with an R2 score of 0.20 and root mean squared error (RMSE) score of 0.087. This paper also found that three unique parking occupancy patterns exist in Amsterdam and that this information, in combination with neighborhood characteristics, has an impact on imputation under certain conditions.

Combining static and portable Cosmic ray neutron sensor data to assess catchment scale heterogeneity in soil water storage and their integrated role in catchment runoff response

Soil water content (SWC) is a key variable in many land surface processes, such as runoff generation, thus knowledge about its spatiotemporal dynamics at the catchment scale can be useful for constraining and evaluating hydrological models. Cosmic ray neutron sensor (CRNS) technology provides hectare scale SWC data, and with recent advances in mobile CRNS such information value can be extended to the catchment scale, although challenges in calibration remain, especially in wet environments. This study presents a new methodology suited for humid environments to explore spatio-temporal variability in near-surface soil water storage (SNS) dynamics at the catchment scale and its value in semi-distributed rainfall-runoff modelling calibration. For a humid mixed-agricultural catchment (~10 km2) in Scotland, we combined ~ 4-years of SWC data from a static CRNS at a landscape-representative location with “snapshots” at four key soil-land use (SLU) units to produce SWC timeseries for each one of those units. The SLU units involved a mixture of freely draining mineral and poorly draining organic-rich soils, supporting crop and livestock farming and moorland, respectively. We also explored the suitability of the standard CRNS calibration approach in the SLU units and found that the organic-rich soils required an adapted parameter calibration for SWC. The moorland SLU unit had the greatest difference in SWC dynamics from the other agricultural SLU units. To explore the additional information generated by the combined CRNS approach, we calibrated a semi-distributed rainfall-runoff model (HBV-light) by using SNS dynamics in individual SLU units in addition to streamflow. Compared to a lumped approach, the semi-distributed SWC information and model structure helped produce better constrained stream flows and further improved the representation of catchment internal storage dynamics. Ultimately, the value of the SWC time series for different SLU units in rainfall-runoff modelling will depend on model structure and the degree to which SNS dynamics vary within the landscape. This study showed the potential of expanding the information value of permanently installed CRNS sensors using portable CRNS surveys while addressing the various challenges related to organic-rich soils and wetter environments, although testing in different environments would be required to evaluate the wider applicability.

Social robot motion planning using contextual distances observed from 3D human motion tracking

The social robot motion planning involves robots operating within the workspace and alongside humans and it is thus necessary for the robots to behave socially. The current literatures make trajectory databases to learn the human trajectory, which has a limited application since only very few out of large possibilities can be recoded. Another approach to the problem is by modelling the macroscopic behaviors, which is limited by parameters that are practically hard to set. Our approach to the problem is thus to understand human motion through a set of simple and well-known primitives, which are observed from the actual human motion. In this paper, we first perform human detection and tracking in a 3D environment. We use a stationary 3D Lidar sensor for the detection and tracking of all moving humans. Our approach detects all moving people and it also solves the problem of occlusion in several cases. We further consider several research hypotheses regarding human navigation noting how much distances the humans prefer to maintain with other humans and obstacles under different common scenarios. The experimentation is done with several subjects and their behavior is used to answer the research hypotheses. A Social Robot Motion Planning algorithm is developed by using a social potential field algorithm as a base. New social forces are added to model the different behaviors displayed by humans. The motion planning algorithm makes the robot maintain the same distances as observed with the humans and perturbating the robots makes them reach the equilibrium distance again.

Galileo precise orbit determination with optical two-way links (OTWL): a continuous wave laser ranging and time transfer concept

In this simulation study we analyze the benefit of ground-space optical two-way links (OTWL) for Galileo precise orbit determination (POD). OTWL is a concept based on continuous wave laser ranging and time transfer with modulated signals from and to ground stations. The measurements are in addition to Global Navigation Satellite System (GNSS) observations. We simulate the measurements with regard to 16 Galileo Sensor Stations. In the simulation study we assume that the whole Galileo satellite constellation is equipped with terminals for OTWL. Using OTWL together with Galileo L-band, in comparison with an orbit solution calculated with L-band-only, demonstrates the advantage of combining two ranging techniques with different influences of systematic errors. The two-way link allows a station and satellite clock synchronization. Furthermore, we compare the ground-space concept with the satellite-to-satellite counterpart known as optical two-way inter-satellite links (OISL). The advantage of OTWL is the connection between the satellite system and the solid Earth as well as the possibility to synchronize the satellite clocks and the ground station clocks. The full network, using all three observation types in combination is simulated as well. The possibility to estimate additional solar radiation pressure (SRP) parameters within these combinations is a clear benefit of these additional links. We paid great attention to simulate systematic effects of all observation techniques as realistically as possible. For L-band these are measurement noise, tropospheric delays, phase center variation of receiver and transmitter antennas, constant and variable biases as well as multipath. For optical links we simulated colored and distance-dependent noise, offsets due to the link repeatability and offsets related to the equipment calibration quality. In addition, we added a troposphere error for the OTWL measurements. We discuss the influence on the formal orbit uncertainties and the effects of the systematic errors. Restrictions due to weather conditions are addressed as well. OTWL is synergetic with the other measurement techniques like OISL and can be used for data transfer and communication, respectively.

Online monitoring of water pollution level based on dissolved oxygen (DO) and biochemical oxygen demand (BOD) using wireless sensor system

In the case of water pollution, including the sea, rivers, lakes, and reservoirs, usually when measuring the parameter of Dissolved Oxygen (DO) and Biochemical Oxygen Demand (BOD) values exceeds the pollution level. Online and continuous monitoring is essential to monitor water conditions so that if there is pollution, it can be immediate handling. Wireless sensor network technology becomes part of the Internet of Things makes it possible for online data acquisition from several node stations that are spread in monitoring locations. This paper purpose the online BOD dan DO data acquisition continuously builds with combine the wireless sensor system and database replication concept. From implementing a database replication method for online monitoring of water pollution level, database replication continuously ensures consistency and prevents redundant data between node sensor station and server database, and provides reliable information on the www website.

Algorithm for Optimal Two-Link Trajectory Planning in Evasion from Detection Problem of Mobile Vehicle with Non-Uniform Radiation Pattern

The problem of an optimal PP/TP (path/trajectory planning) evasion of a mobile vehicle from the detection is considered. The objective is to minimize the risk of a moving vehicle being detected by a static sensor when moving between two specified points on the plane. Detection is based on the primary acoustic field emitted by a vehicle with an inhomogeneous radiation pattern. An algorithm for finding a two-link optimal trajectory is proposed. The optimal trajectory and the law of speed of a mobile vehicle, as well as the value of the criterion, are found.